Nettyfy Technologies

AI Driven Video Analytics Software project focused on developing a system that utilizes computer vision and machine learning techniques to detect, track, and analyze people in a retail environment. The system aims to gather information about customer behavior, footfall patterns, and in-store movement to provide valuable insights for retailers, improving store operations and customer experience.Technology Stack:Microsoft Azure IoT SuiteOpenPoseYOLOTensorFlowDeepSortOpenCVDlibScikit-learnPyTorchKerasImageAIMatplotlibBusiness Result:Improved Store Layout: Insights into customer movement and behavior will allow retailers to optimize store layout and product placement for maximum efficiency and sales.Enhanced Customer Experience: Understanding customer preferences and behavior will enable retailers to provide tailored shopping experiences and improve overall customer satisfaction.Optimized Staff Allocation: The detection system will help retailers allocate staff more effectively based on footfall patterns and customer interactions.Increased Sales: By understanding customer behavior and optimizing store operations, retailers can drive sales and increase revenue.Real-Time Insights: Retailers will have access to real-time insights and analytics, allowing them to make informed decisions and respond quickly to customer needs.Security and Surveillance: The people detection system will also serve as an additional layer of security and surveillance within the retail mall, contributing to a safer shopping environment.

The aim of this project was to design and develop an AI Trading Bot capable of making informed and profitable trading decisions in financial markets. The AI Trading Bot should leverage advanced machine learning and artificial intelligence techniques to analyze large volumes of historical and real-time market data, identify patterns and trends, and make accurate predictions about the future movements of various financial instruments such as stocks, commodities, or cryptocurrencies.Technology Stack:Machine LearningTime Series AnalysisSRIMA ModelNLPCloud ComputingPythonReactAWSDeep Learning

This project involves creating a conversational AI voice bot for restaurants to automate the process of accepting table reservations and food orders over the telephone. The voice bot will be able to understand customer requests, check availability in real-time, gather required information, and confirm bookings or orders. The project aims to improve customer service, reduce the workload on restaurant staff, and streamline operations.Tools & Libraries:TensorFlowPyTorchTransformersNLPNLUNLGSpacyDjangoNodeJSRoBERTaBusiness Results:Improved Customer Experience: The voice bot will provide immediate responses and assistance to customers, enhancing the overall customer experience.Operational Efficiency: Automating the process of table reservations and food orders will reduce the workload on restaurant staff and streamline operations.24/7 Availability: The voice bot will offer round-the-clock service, allowing customers to make reservations or place orders at any time.Increased Revenue: The voice bot can upsell certain products during the food ordering process, potentially increasing revenue for the restaurant.Data Insights: The voice bot will collect data on customer interactions, providing valuable insights into customer preferences and trends, and potentially informing future business strategies.

B2B Trading App for PVC Raw Material Company, a leading player in the PVC raw material trading industry, recognized the need to digitize their trading operations to enhance efficiency and gain a competitive edge. With a wide network of suppliers and buyers across the globe, they sought a comprehensive solution that could simplify the trading process, facilitate seamless communication, and enable secure transactions. Technology Stack:Node JsReactNext JsAWS EC2, S3, ELB, RDS, Route53, Cloud FrontPythonMySQLResult The implementation of the B2B trading app produced impressive metrics that demonstrated the success and impact of the solution on Puri Polymer’s business performance. Key statistics include:35% reduction in trading discrepancies and disputes, leading to increased operational efficiency.25% increase in average profit margins due to data-driven pricing strategies.80% growth in the customer base, expanding market reach and enhancing revenue opportunities.20% improvement in customer retention rates, indicating higher customer satisfaction and loyalty.